9 accepted papers
Unlike traditional bit-level data transmission methods, semantic communication focuses on conveying the meaning behind the data. Though promising results have been achieved, existing end-to-end learning-based semantic communication frameworks often require a synchronization of deep models between th…
In recent years, Text-to-Image (T2I) models have made remarkable advancements, yet accurate accurate association of attributes remains a key challenge. This paper presents FreeAlign, a novel training-free framework designed to enhance attribute alignment in T2I generation. By modulating attention an…
Given a complex text description, existing studies have indeed made significant progress in motion synthesis, which largely overlook the fine-grained action information from expressive texts. In this paper, we introduce LGDDM, a LLM-Guided Dual-branch Diffusion Model, which relies on the LLM’s abili…
Although diffusion models advance condition-based visual generation, they suffer from speed and cost issues, unlike faster AutoRegressive methods that are limited in performance. To address these, we introduce the Stable Control Visual AutoRegressive Model (SCVAR). SCVAR ensures stable control by al…
Diffusion models excel at audio-driven gesture generation. However, the reverse denoising process is computationally intensive and time-redundant. Additionally, existing methods predominantly focus on the quality and diversity of upper-body gestures, without considering the movement trajectories of…
Monitoring multimodal signals provides a more comprehensive understanding of health conditions compared to singlemode monitoring. In the face of the significant volumes of multimodal signals, existing IoT health monitoring systems primarily focus on high-fidelity signal transmission by encoding mult…
In recent years, semantic communication based on deep learning for source-channel joint encoding has garnered significant attention. It utilizes network models trained end-to-end to represent signals as embedding vectors and has demonstrated superior performance compared to traditional methods. Howe…
To acquire a snapshot spectral image, coded aperture snapshot spectral imaging (CASSI) is proposed. A core problem of the CASSI system is to recover the reliable and fine underlying 3D spectral cube from the 2D measurement. By alternately solving a data subproblem and a prior subproblem, deep unfold…
We propose a novel dual-camera design to acquire 4D high-speed hyperspectral (HSHS) videos with high spatial and spectral resolution. Our work has two key technical contributions. First, we build a dual-camera system that simultaneously captures a panchromatic video at a high frame rate and a hypers…